Explainable Artificial Intelligence (XAI) for Population Health Management – An Appraisal


  •   Het Naik

  •   Priyanka Goradia

  •   Vomini Desai

  •   Yukta Desai, MS

  •   Muralikrishna Iyyanki


This study explores Explainable Artificial Intelligence (XAI) in general and then talked about its potential use for the India Healthcare system. It also demonstrated some XAI techniques on a diabetes dataset with an aim to show practical implementation and implore the readers to think about more application areas. However, there are certain limitations of the technology which are highlighted along with the future scope in the discussion.

Keywords: Artificial Intelligence (AI), Chronic disease management, Decision Support System, Explainable Artificial Intelligence (XAI)


Dhurandhar A, Chen Y, Luss R, Tu C, Ting P, Shanmugam K, Das P. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. Advances in Neural Information Processing Systems, 2018;592–603.

Garc ??a V, Aznarte L. Shapley additive explanations for no2 forecasting. Ecological Informatics, 2020:56.

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2017; 2 (4): 230–243.

Looveren A.V, Klaise J. Interpretable counterfactual explanations guided by prototypes. 2020.

Lundberg S.M, Lee S.I. A unified approach to interpreting model predictions. Advances in neural information processing systems, 2017: 4765–4774.

Pawar U, O’shea D, Rea S, O’Reilly R. Explainable AI in healthcare. Reasonable Explainability for Regulating AI in Health. June 2020.

Ribeiro M.T, Singh S, Guestrin C. Anchors: High-precision model-agnostic Explanations. AAAI Conference on Artificial Intelligence (AAAI). 2018.

Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks. arXiv preprint arXiv.2017;1703.01365.

Thampi A. Interpretable AI, Building explainable machine learning systems. Manning Publications, 2020.

Wachter S, Mittelstadt B, Russell C.Counterfactual explanations without opening the black box: Automated decisions and the gdpr. 2018.

Alex A., Freitas. Comprehensible Classification Models: A Position Paper. SIGKDD Explor. Newsl. 15.2014; 1:1–10.

Friedman J.Greedy function approximation: a gradient boosting machine. Annals of statistics, 2001; 1189–1232.

Friedman J, Popescu B. Predictive learning via rule ensemble. The Annals of Applied Statistics 2, 2008; 3: 916–954.

Gade K, Geyik S, Kenthapadi K, Mithal V, Taly A. Explainable AI in Industry. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19) Association for Computing Machinery.2019; 3203–3204.

García M, a José L Aznarte. Shapley additive explanations for NO2 forecasting. Ecological Informatics, 2020;56(101039).

Patel V, Mazumdar-Shaw K, Kang G, Das P, Khanna T. Reimagining India's health system: a Lancet Citizens’ Commission. The Lancet, 2021;397(10283):427-1430.

Kaushik A, Raman A. The new data-driven enterprise architecture for e-healthcare: Lessons from the Indian public sector. Government Information Quarterly, 2015;32(1): 63-74.

Dhagarra D, Goswami M, Kumar G. Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective. International Journal of Medical Informatics, 2020;41(104164: ISSN 1386-5056.


Download data is not yet available.


How to Cite
Naik, H., Goradia, P., Desai, V., Desai, Y. and Iyyanki, M. 2021. Explainable Artificial Intelligence (XAI) for Population Health Management – An Appraisal. European Journal of Electrical Engineering and Computer Science. 5, 6 (Dec. 2021), 64–76. DOI:https://doi.org/10.24018/ejece.2021.5.6.368.